{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Dropout\n",
    "Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n",
    "\n",
    "[1] Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_val:  (1000,)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n",
      "X_val:  (1000, 3, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Dropout forward pass\n",
    "In the file `cs231n/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n",
    "\n",
    "Once you have done so, run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running tests with p =  0.3\n",
      "Mean of input:  10.0002078785\n",
      "Mean of train-time output:  10.0350727971\n",
      "Mean of test-time output:  9.98111626229\n",
      "Fraction of train-time output set to zero:  0.699124\n",
      "Fraction of test-time output set to zero:  0.700504\n",
      "\n",
      "Running tests with p =  0.6\n",
      "Mean of input:  10.0002078785\n",
      "Mean of train-time output:  9.97779296231\n",
      "Mean of test-time output:  10.0085321069\n",
      "Fraction of train-time output set to zero:  0.401252\n",
      "Fraction of test-time output set to zero:  0.399452\n",
      "\n",
      "Running tests with p =  0.75\n",
      "Mean of input:  10.0002078785\n",
      "Mean of train-time output:  9.9951159931\n",
      "Mean of test-time output:  9.9908649677\n",
      "Fraction of train-time output set to zero:  0.25062\n",
      "Fraction of test-time output set to zero:  0.250584\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(500, 500) + 10\n",
    "\n",
    "for p in [0.3, 0.6, 0.75]:\n",
    "  out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n",
    "  out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n",
    "\n",
    "  print('Running tests with p = ', p)\n",
    "  print('Mean of input: ', x.mean())\n",
    "  print('Mean of train-time output: ', out.mean())\n",
    "  print('Mean of test-time output: ', out_test.mean())\n",
    "  print('Fraction of train-time output set to zero: ', (out == 0).mean())\n",
    "  print('Fraction of test-time output set to zero: ', (out_test == 0).mean())\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Dropout backward pass\n",
    "In the file `cs231n/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx relative error:  5.44561271827e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10) + 10\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123}\n",
    "out, cache = dropout_forward(x, dropout_param)\n",
    "dx = dropout_backward(dout, cache)\n",
    "dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n",
    "\n",
    "print('dx relative error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fully-connected nets with Dropout\n",
    "In the file `cs231n/classifiers/fc_net.py`, modify your implementation to use dropout. Specificially, if the constructor the the net receives a nonzero value for the `dropout` parameter, then the net should add dropout immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with dropout =  0\n",
      "Initial loss:  2.30047908977\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "\n",
      "Running check with dropout =  0.25\n",
      "Initial loss:  2.29243250883\n",
      "W1 relative error: 2.74e-08\n",
      "W2 relative error: 2.98e-09\n",
      "W3 relative error: 4.29e-09\n",
      "b1 relative error: 7.78e-10\n",
      "b2 relative error: 3.36e-10\n",
      "b3 relative error: 1.65e-10\n",
      "\n",
      "Running check with dropout =  0.5\n",
      "Initial loss:  2.30427592208\n",
      "W1 relative error: 3.11e-07\n",
      "W2 relative error: 1.84e-08\n",
      "W3 relative error: 5.35e-08\n",
      "b1 relative error: 5.37e-09\n",
      "b2 relative error: 2.99e-09\n",
      "b3 relative error: 9.54e-11\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for dropout in [0, 0.25, 0.5]:\n",
    "  print('Running check with dropout = ', dropout)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            weight_scale=5e-2, dtype=np.float64,\n",
    "                            dropout=dropout, seed=123)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Regularization experiment\n",
    "As an experiment, we will train a pair of two-layer networks on 500 training examples: one will use no dropout, and one will use a dropout probability of 0.75. We will then visualize the training and validation accuracies of the two networks over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "(Iteration 1 / 125) loss: 7.856644\n",
      "(Epoch 0 / 25) train acc: 0.274000; val_acc: 0.192000\n",
      "(Epoch 1 / 25) train acc: 0.410000; val_acc: 0.263000\n",
      "(Epoch 2 / 25) train acc: 0.518000; val_acc: 0.269000\n",
      "(Epoch 3 / 25) train acc: 0.550000; val_acc: 0.248000\n",
      "(Epoch 4 / 25) train acc: 0.684000; val_acc: 0.297000\n",
      "(Epoch 5 / 25) train acc: 0.758000; val_acc: 0.292000\n",
      "(Epoch 6 / 25) train acc: 0.782000; val_acc: 0.266000\n",
      "(Epoch 7 / 25) train acc: 0.862000; val_acc: 0.240000\n",
      "(Epoch 8 / 25) train acc: 0.864000; val_acc: 0.284000\n",
      "(Epoch 9 / 25) train acc: 0.896000; val_acc: 0.280000\n",
      "(Epoch 10 / 25) train acc: 0.890000; val_acc: 0.265000\n",
      "(Epoch 11 / 25) train acc: 0.952000; val_acc: 0.295000\n",
      "(Epoch 12 / 25) train acc: 0.964000; val_acc: 0.294000\n",
      "(Epoch 13 / 25) train acc: 0.958000; val_acc: 0.281000\n",
      "(Epoch 14 / 25) train acc: 0.976000; val_acc: 0.276000\n",
      "(Epoch 15 / 25) train acc: 0.978000; val_acc: 0.291000\n",
      "(Epoch 16 / 25) train acc: 0.988000; val_acc: 0.302000\n",
      "(Epoch 17 / 25) train acc: 0.982000; val_acc: 0.308000\n",
      "(Epoch 18 / 25) train acc: 0.986000; val_acc: 0.311000\n",
      "(Epoch 19 / 25) train acc: 0.986000; val_acc: 0.302000\n",
      "(Epoch 20 / 25) train acc: 0.994000; val_acc: 0.290000\n",
      "(Iteration 101 / 125) loss: 0.009690\n",
      "(Epoch 21 / 25) train acc: 0.996000; val_acc: 0.309000\n",
      "(Epoch 22 / 25) train acc: 0.976000; val_acc: 0.315000\n",
      "(Epoch 23 / 25) train acc: 0.982000; val_acc: 0.306000\n",
      "(Epoch 24 / 25) train acc: 0.988000; val_acc: 0.276000\n",
      "(Epoch 25 / 25) train acc: 0.996000; val_acc: 0.288000\n",
      "0.75\n",
      "(Iteration 1 / 125) loss: 11.299055\n",
      "(Epoch 0 / 25) train acc: 0.234000; val_acc: 0.175000\n",
      "(Epoch 1 / 25) train acc: 0.400000; val_acc: 0.239000\n",
      "(Epoch 2 / 25) train acc: 0.458000; val_acc: 0.257000\n",
      "(Epoch 3 / 25) train acc: 0.574000; val_acc: 0.240000\n",
      "(Epoch 4 / 25) train acc: 0.630000; val_acc: 0.264000\n",
      "(Epoch 5 / 25) train acc: 0.684000; val_acc: 0.306000\n",
      "(Epoch 6 / 25) train acc: 0.672000; val_acc: 0.266000\n",
      "(Epoch 7 / 25) train acc: 0.766000; val_acc: 0.266000\n",
      "(Epoch 8 / 25) train acc: 0.792000; val_acc: 0.262000\n",
      "(Epoch 9 / 25) train acc: 0.848000; val_acc: 0.286000\n",
      "(Epoch 10 / 25) train acc: 0.826000; val_acc: 0.285000\n",
      "(Epoch 11 / 25) train acc: 0.876000; val_acc: 0.259000\n",
      "(Epoch 12 / 25) train acc: 0.894000; val_acc: 0.277000\n",
      "(Epoch 13 / 25) train acc: 0.894000; val_acc: 0.281000\n",
      "(Epoch 14 / 25) train acc: 0.890000; val_acc: 0.287000\n",
      "(Epoch 15 / 25) train acc: 0.910000; val_acc: 0.315000\n",
      "(Epoch 16 / 25) train acc: 0.936000; val_acc: 0.274000\n",
      "(Epoch 17 / 25) train acc: 0.920000; val_acc: 0.273000\n",
      "(Epoch 18 / 25) train acc: 0.938000; val_acc: 0.300000\n",
      "(Epoch 19 / 25) train acc: 0.920000; val_acc: 0.259000\n",
      "(Epoch 20 / 25) train acc: 0.950000; val_acc: 0.282000\n",
      "(Iteration 101 / 125) loss: 0.762737\n",
      "(Epoch 21 / 25) train acc: 0.942000; val_acc: 0.315000\n",
      "(Epoch 22 / 25) train acc: 0.930000; val_acc: 0.315000\n",
      "(Epoch 23 / 25) train acc: 0.954000; val_acc: 0.278000\n",
      "(Epoch 24 / 25) train acc: 0.954000; val_acc: 0.289000\n",
      "(Epoch 25 / 25) train acc: 0.960000; val_acc: 0.300000\n"
     ]
    }
   ],
   "source": [
    "# Train two identical nets, one with dropout and one without\n",
    "np.random.seed(231)\n",
    "num_train = 500\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "dropout_choices = [0, 0.75]\n",
    "for dropout in dropout_choices:\n",
    "  model = FullyConnectedNet([500], dropout=dropout)\n",
    "  print(dropout)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=25, batch_size=100,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-4,\n",
    "                  },\n",
    "                  verbose=True, print_every=100)\n",
    "  solver.train()\n",
    "  solvers[dropout] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ATpKk809e3gymlOjqeilHj75/yWPWr38JDz/8Vx33/eS5c2+xvDxfJEnLy9Pr+fxwyKm9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\nlcgEnCRJkiRJklQiE3CSJEmSJElSif4Xn0qaAFc3VFIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20372accf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot train and validation accuracies of the two models\n",
    "\n",
    "train_accs = []\n",
    "val_accs = []\n",
    "for dropout in dropout_choices:\n",
    "  solver = solvers[dropout]\n",
    "  train_accs.append(solver.train_acc_history[-1])\n",
    "  val_accs.append(solver.val_acc_history[-1])\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "for dropout in dropout_choices:\n",
    "  plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n",
    "plt.title('Train accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "  \n",
    "plt.subplot(3, 1, 2)\n",
    "for dropout in dropout_choices:\n",
    "  plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n",
    "plt.title('Val accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Question\n",
    "Explain what you see in this experiment. What does it suggest about dropout?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Answer\n"
   ]
  }
 ],
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